Cerebral stroke is a major cause of mortality and morbidity in many countries. Prompt assessment and treatment of stroke helps patients affected by this disease to recover some neurological function that may have been lost during the acute phase. Besides classical magnetic resonance imaging (MRI) sequences, two other modalities have been used to evaluate acute stroke patients, namely perfusion imaging (PI) and diffusion-weighted imaging (DWI).
Perfusion imaging (PI) involves hemo-dynamically weighted MR sequences that are based on the passage of magnetic resonance (MR) contrast through brain tissue. Measurements of brain perfusion include vascular transit time, cerebral blood volume, and cerebral blood flow. Serial analysis of arterial input is observed to determine absolute cerebral blood flow. This typically involves measuring relative blood flow and comparing the two hemispheres of the brain for regional differences.
Diffusion-weighted imaging involves images that reflect microscopic random motion of water molecules. Water molecules are in constant motion, and the rate of diffusion depends on the energy of the molecules, which is temperature dependent. However, diffusion is not really random, because of tissue structure. Cell membranes and vascular structures, for example, limit diffusion. In the study of acute strokes, DWI abnormalities are markers of critical ischemia, which typically evolve into infarction.
To obtain diffusion-weighted images, a pair of strong gradient pulses is added to a pulse sequence. The first pulse de-phases the spins, and the second pulse re-phases the spins if no net movement occurs. If net movement of spins occurs between the gradient pulses, signal attenuation occurs. The degree of attenuation depends on the magnitude of molecular translation and diffusion weighting. The amount of diffusion weighting is determined by the strength of the diffusion gradients, the duration of the gradients, and the time between the gradient pulses.
Automatic or semiautomatic tools for segmentation and evaluation of acute stroke regions have been suggested. Fast and accurate segmentation of acute infarct is crucial for evaluation and treatment of stroke patients.
Martel A L, Allder S J, Delay G S, Morgan P S and Moody A R, “Measurement of infarct volume in stroke patients using adaptive segmentation of diffusion weighted MR images,” MICCAI, 1679, 22-31, 1999, describes a semi-automatic method to determine infarct volume by diffusion tensor-MRI. The method uses an adaptive threshold algorithm, which incorporates a spatial constraint, to segment images.
Wu Li, Jie Tan, Enzhong Li and Jianping Dai “Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification,” Neuroimage, 23, 1507-1518, 2004, have proposed an unsupervised segmentation method using multiscale statistical classification and partial volume voxel reclassification in the case of diffusion tensor MR images. They attempt to identify the infarct regions and overcome the problem of intensity overlapping caused by diffusion anisotropy, and reclassify partial volume voxels. The method is said to be robust to noise and RF inhomogeneities. This method uses DTI volumes to segment and eliminate the artifacts. The computational complexity is high.
In clinical practice, the analysis of DWI-PI datasets is based on manual image editing and segmentation techniques provided by available commercial medical software. A complete study takes from 15 to 20 minutes of user interaction; see Bardera A, Boada I, Feixas M, Pedraza S, and Rodriguez J, “A Framework to Assist Acute Stroke Diagnosis,” Vision, Modeling, and Visualization (VMV 2005)—Article No. 666, Erlangen, Germany, Nov. 16-18, 2005.
Thus, a need clearly exists for a segmentation algorithm that is both less computationally expensive and faster (capable of completing in less than a minute).